With China's continuous opening to the outside world, changes in international environment and operation of cross-border RMB settlement system (CIPS), scale has fluctuated continuously. In response this phenomenon, authors collected sorted out total amount payments from 2011 2020 time sequence data China, then use five AR models including ARMA, GARCH(1.1), EGARCH(1.1), PARCH(1.1), CARCH(1.1) fi...

4. Course Outline: (i) Review of Linear ARMA/ARIMA Time Series Models and their Properties. (ii) An Introduction to Spectral Analysis of Time Series. (iii) Fractional Differencing and Long Memory Time Series Modelling. (iv) Generalized Fractional Processes. Gegenbaur Processes. (v) Topics from Financial Time Series/Econometrics: ARCH and GARCH Models. (vi ) Time Series Modelling of Durations: A...

Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...

Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability provide certain degree of fading mitigation and improve spectral efficiency. In cooperative scenario, the intercept probability system can be reduced by optimizing relay selection scheme order select optimal from set available relays for data transmission. However, due mobility WVN...

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observatio...

The adequate modeling and estimation of solar radiation plays a vital role in designing energy applications. In fact, unnecessary environmental changes result several problems with the components photovoltaic affects generation network. Various computational algorithms have been developed over past decades to improve efficiency predicting various input characteristics. This research provides fi...

We consider computationally-fast methods for estimating parameters in ARMA processes from binary time series data, obtained by thresholding the latent ARMA process. All methods involve matching estimated and expected autocorrelations of the binary series. In particular, we focus on the spectral representation of the likelihood of an ARMA process and derive a restricted form of this likelihood, ...

A feasibility study of using of Dynamic Bayesian Networks in combination with ARMA modeling in exchange rate prediction is presented. A new algorithm (ARMA-DBN) is constructed and applied to the exchange rate forecast of RMB. Results show that the improved dynamic Bayesian forecast algorithm has better performance than the standard ARMA model.

Presented in this paper is a novel methodology to determine the average number of transitions in a signal from its word-level statistical description. The proposed methodology employs: 1) high-level signal statistics, 2) a statistical signal generation model, and 3) the signal encoding (or number representation) to estimate the transition activity for that signal. In particular, the signal stat...